Package: imageseg 0.5.1

Juergen Niedballa

imageseg: Deep Learning Models for Image Segmentation

A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <arxiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <arxiv:1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.

Authors:Juergen Niedballa [aut, cre], Jan Axtner [aut], Leibniz Institute for Zoo and Wildlife Research [cph]

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NEWS

Peer review:

Bug tracker:https://github.com/ecodynizw/imageseg/issues

On CRAN:

image-segmentationkerastensorflow

9 exports 19 stars 2.01 score 46 dependencies 9 scripts 159 downloads

Last updated 10 months agofrom:ebf161bdf3. Checks:OK: 7. Indexed: yes.

Exports:dataAugmentationfindValidRegionimageSegmentationimagesToKerasInputloadImagesloadModelresizeImagesu_netu_net_plusplus

Dependencies:backportsbase64encclicodetoolsconfigcurldoParalleldplyrfansiforeachgenericsgluehereiteratorsjsonlitekeraslatticelifecyclemagickmagrittrMatrixpillarpkgconfigpngprocessxpspurrrR6rappdirsRcppRcppTOMLreticulaterlangrprojrootrstudioapitensorflowtfautographtfrunstibbletidyselectutf8vctrswhiskerwithryamlzeallot